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Name Game

You can find the complete source code for this model here.

The Data set consists of name, gender, state and year with the number of people that were born with that name in that gender, state and year.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  top: 10
  project: *
}
QUERY RESULTS
stategenderyearnamenumber
AKF1,910Elsie6
ALF1,910Annie482
ALF1,910Katie84
ALF1,910Alma82
ALF1,910Thelma65
ALF1,910Ellen38
ALF1,910Rebecca35
ALF1,910Estella21
ALF1,910Corine17
ALF1,910Margie17
[
  {
    "state": "AK",
    "gender": "F",
    "year": 1910,
    "name": "Elsie",
    "number": 6
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Annie",
    "number": 482
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Katie",
    "number": 84
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Alma",
    "number": 82
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Thelma",
    "number": 65
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Ellen",
    "number": 38
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Rebecca",
    "number": 35
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Estella",
    "number": 21
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Corine",
    "number": 17
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Margie",
    "number": 17
  }
]
SELECT 
   base.state as state,
   base.gender as gender,
   base.year as year,
   base.name as name,
   base.number as number
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
LIMIT 10

The command above says query the table 'bigquery-public-data.usa_names.usa_1910_2013' and project (show) all the columns for the first 10 rows.

Grouping a query

In SQL there are basically two kinds of SELECT commands: SELECT ... GROUP BY and SELECT without a grouping. In Malloy, these are two different commands. The command in Malloy for SELECT ... GROUP BY is group_by. Since number and year are reserved words, we have to quote the names with back-tics.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  group_by: name
  aggregate: population is `number`.sum()
}
QUERY RESULTS
namepopulation
James4,942,431
John4,834,422
Robert4,718,787
Michael4,297,230
William3,822,209
Mary3,737,679
David3,549,801
Richard2,531,924
Joseph2,472,917
Charles2,244,693
Thomas2,212,781
Christopher1,987,490
Daniel1,842,964
Patricia1,569,570
Matthew1,538,689
Elizabeth1,492,404
Jennifer1,463,145
Linda1,447,601
Barbara1,424,203
Donald1,402,666
Anthony1,385,453
Paul1,355,711
Mark1,339,371
George1,321,652
Steven1,269,627
Kenneth1,259,720
Andrew1,228,080
Edward1,210,315
Joshua1,163,481
Brian1,156,495
Kevin1,148,680
Margaret1,120,766
Susan1,108,712
Ronald1,074,141
Timothy1,055,792
Dorothy1,052,570
Jessica1,038,359
Jason1,009,416
Sarah1,006,934
Betty985,364
Nancy984,185
Karen982,192
Jeffrey969,093
Lisa962,981
Ryan907,969
Gary896,637
Helen895,428
Sandra870,958
Nicholas867,231
Eric862,204
Jacob848,518
Stephen844,930
Ashley841,427
Donna826,714
Kimberly826,275
Frank818,197
Carol817,290
Jonathan803,723
Michelle803,162
Larry801,398
Emily779,755
Amanda769,292
Scott765,844
Justin757,273
Laura745,892
Melissa745,498
Raymond744,284
Deborah735,869
Brandon735,849
Stephanie732,651
Rebecca728,799
Ruth728,678
Anna719,803
Sharon718,701
Kathleen703,265
Cynthia703,175
Gregory701,504
Shirley686,079
Samuel681,358
Amy673,234
Benjamin664,328
Patrick655,434
Angela653,663
Jack634,113
Virginia621,448
Jerry618,105
Dennis610,894
Catherine606,029
Brenda604,067
Alexander598,944
Katherine593,220
Pamela591,307
Nicole576,870
Tyler576,031
Christine574,120
Henry571,539
Walter558,026
Peter553,789
Douglas552,666
Janet550,557
[
  {
    "name": "James",
    "population": 4942431
  },
  {
    "name": "John",
    "population": 4834422
  },
  {
    "name": "Robert",
    "population": 4718787
  },
  {
    "name": "Michael",
    "population": 4297230
  },
  {
    "name": "William",
    "population": 3822209
  },
  {
    "name": "Mary",
    "population": 3737679
  },
  {
    "name": "David",
    "population": 3549801
  },
  {
    "name": "Richard",
    "population": 2531924
  },
  {
    "name": "Joseph",
    "population": 2472917
  },
  {
    "name": "Charles",
    "population": 2244693
  },
  {
    "name": "Thomas",
    "population": 2212781
  },
  {
    "name": "Christopher",
    "population": 1987490
  },
  {
    "name": "Daniel",
    "population": 1842964
  },
  {
    "name": "Patricia",
    "population": 1569570
  },
  {
    "name": "Matthew",
    "population": 1538689
  },
  {
    "name": "Elizabeth",
    "population": 1492404
  },
  {
    "name": "Jennifer",
    "population": 1463145
  },
  {
    "name": "Linda",
    "population": 1447601
  },
  {
    "name": "Barbara",
    "population": 1424203
  },
  {
    "name": "Donald",
    "population": 1402666
  },
  {
    "name": "Anthony",
    "population": 1385453
  },
  {
    "name": "Paul",
    "population": 1355711
  },
  {
    "name": "Mark",
    "population": 1339371
  },
  {
    "name": "George",
    "population": 1321652
  },
  {
    "name": "Steven",
    "population": 1269627
  },
  {
    "name": "Kenneth",
    "population": 1259720
  },
  {
    "name": "Andrew",
    "population": 1228080
  },
  {
    "name": "Edward",
    "population": 1210315
  },
  {
    "name": "Joshua",
    "population": 1163481
  },
  {
    "name": "Brian",
    "population": 1156495
  },
  {
    "name": "Kevin",
    "population": 1148680
  },
  {
    "name": "Margaret",
    "population": 1120766
  },
  {
    "name": "Susan",
    "population": 1108712
  },
  {
    "name": "Ronald",
    "population": 1074141
  },
  {
    "name": "Timothy",
    "population": 1055792
  },
  {
    "name": "Dorothy",
    "population": 1052570
  },
  {
    "name": "Jessica",
    "population": 1038359
  },
  {
    "name": "Jason",
    "population": 1009416
  },
  {
    "name": "Sarah",
    "population": 1006934
  },
  {
    "name": "Betty",
    "population": 985364
  },
  {
    "name": "Nancy",
    "population": 984185
  },
  {
    "name": "Karen",
    "population": 982192
  },
  {
    "name": "Jeffrey",
    "population": 969093
  },
  {
    "name": "Lisa",
    "population": 962981
  },
  {
    "name": "Ryan",
    "population": 907969
  },
  {
    "name": "Gary",
    "population": 896637
  },
  {
    "name": "Helen",
    "population": 895428
  },
  {
    "name": "Sandra",
    "population": 870958
  },
  {
    "name": "Nicholas",
    "population": 867231
  },
  {
    "name": "Eric",
    "population": 862204
  },
  {
    "name": "Jacob",
    "population": 848518
  },
  {
    "name": "Stephen",
    "population": 844930
  },
  {
    "name": "Ashley",
    "population": 841427
  },
  {
    "name": "Donna",
    "population": 826714
  },
  {
    "name": "Kimberly",
    "population": 826275
  },
  {
    "name": "Frank",
    "population": 818197
  },
  {
    "name": "Carol",
    "population": 817290
  },
  {
    "name": "Jonathan",
    "population": 803723
  },
  {
    "name": "Michelle",
    "population": 803162
  },
  {
    "name": "Larry",
    "population": 801398
  },
  {
    "name": "Emily",
    "population": 779755
  },
  {
    "name": "Amanda",
    "population": 769292
  },
  {
    "name": "Scott",
    "population": 765844
  },
  {
    "name": "Justin",
    "population": 757273
  },
  {
    "name": "Laura",
    "population": 745892
  },
  {
    "name": "Melissa",
    "population": 745498
  },
  {
    "name": "Raymond",
    "population": 744284
  },
  {
    "name": "Deborah",
    "population": 735869
  },
  {
    "name": "Brandon",
    "population": 735849
  },
  {
    "name": "Stephanie",
    "population": 732651
  },
  {
    "name": "Rebecca",
    "population": 728799
  },
  {
    "name": "Ruth",
    "population": 728678
  },
  {
    "name": "Anna",
    "population": 719803
  },
  {
    "name": "Sharon",
    "population": 718701
  },
  {
    "name": "Kathleen",
    "population": 703265
  },
  {
    "name": "Cynthia",
    "population": 703175
  },
  {
    "name": "Gregory",
    "population": 701504
  },
  {
    "name": "Shirley",
    "population": 686079
  },
  {
    "name": "Samuel",
    "population": 681358
  },
  {
    "name": "Amy",
    "population": 673234
  },
  {
    "name": "Benjamin",
    "population": 664328
  },
  {
    "name": "Patrick",
    "population": 655434
  },
  {
    "name": "Angela",
    "population": 653663
  },
  {
    "name": "Jack",
    "population": 634113
  },
  {
    "name": "Virginia",
    "population": 621448
  },
  {
    "name": "Jerry",
    "population": 618105
  },
  {
    "name": "Dennis",
    "population": 610894
  },
  {
    "name": "Catherine",
    "population": 606029
  },
  {
    "name": "Brenda",
    "population": 604067
  },
  {
    "name": "Alexander",
    "population": 598944
  },
  {
    "name": "Katherine",
    "population": 593220
  },
  {
    "name": "Pamela",
    "population": 591307
  },
  {
    "name": "Nicole",
    "population": 576870
  },
  {
    "name": "Tyler",
    "population": 576031
  },
  {
    "name": "Christine",
    "population": 574120
  },
  {
    "name": "Henry",
    "population": 571539
  },
  {
    "name": "Walter",
    "population": 558026
  },
  {
    "name": "Peter",
    "population": 553789
  },
  {
    "name": "Douglas",
    "population": 552666
  },
  {
    "name": "Janet",
    "population": 550557
  }
]
SELECT 
   base.name as name,
   COALESCE(SUM(base.number),0) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc

Malloy compiles to SQL. The SQL query for the above command is.

SELECT
   base.name as name,
   SUM(base.number) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc

Expressions

Expressions work much the same as they do in SQL. We can look at population over decade by using a calculation against the year.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  top: 10
  group_by: decade is floor(`year` / 10) * 10
  aggregate: population is sum(`number`)
}
QUERY RESULTS
decadepopulation
1,95037,545,138
1,96035,405,002
1,99033,309,594
2,00033,041,629
1,98032,349,146
1,97029,238,092
1,94027,776,913
1,92021,375,400
1,93019,782,373
1,91013,592,350
[
  {
    "decade": 1950,
    "population": 37545138
  },
  {
    "decade": 1960,
    "population": 35405002
  },
  {
    "decade": 1990,
    "population": 33309594
  },
  {
    "decade": 2000,
    "population": 33041629
  },
  {
    "decade": 1980,
    "population": 32349146
  },
  {
    "decade": 1970,
    "population": 29238092
  },
  {
    "decade": 1940,
    "population": 27776913
  },
  {
    "decade": 1920,
    "population": 21375400
  },
  {
    "decade": 1930,
    "population": 19782373
  },
  {
    "decade": 1910,
    "population": 13592350
  }
]
SELECT 
   (floor(base.year/10))*10 as decade,
   COALESCE(SUM(base.number),0) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc
LIMIT 10

Cohorts 

source: names is from(
  table('bigquery-public-data.usa_names.usa_1910_2013') -> {
    group_by:
      decade is floor(`year` / 10) * 10
      state
      gender
    aggregate: cohort_population is `number`.sum()
    nest: by_name is {
      group_by: name
      aggregate: population is `number`.sum()
    }
  }
) {
  measure: births_per_100k is floor(by_name.population.sum() / cohort_population.sum() * 100000)
  measure: total_cohort_population is cohort_population.sum()
  dimension: name is concat(by_name.name, '')
}

query: names -> {
  group_by:
    state
    gender
  aggregate: total_cohort_population
  nest: names is {
    where: name  ? 'Michael' | 'Lloyd' | 'Olivia'
    group_by: name
    aggregate: population is by_name.population.sum()
    group_by: births_per_100k
  }
}
QUERY RESULTS
stategendertotal_​cohort_​populationnames
CAM15,011,405
namepopulationbirths_​per_​100k
Michael420,1272,798
Lloyd8,38055
CAF13,799,158
namepopulationbirths_​per_​100k
Olivia33,794244
Michael2,42819
NYM12,203,961
namepopulationbirths_​per_​100k
Michael430,7323,529
Lloyd7,56862
Olivia50
NYF11,490,175
namepopulationbirths_​per_​100k
Olivia23,219202
Michael1,76116
TXM11,126,246
namepopulationbirths_​per_​100k
Michael223,9002,012
Lloyd8,46176
Olivia61
TXF10,341,113
namepopulationbirths_​per_​100k
Olivia25,746248
Michael1,42016
Lloyd151
PAM8,497,961
namepopulationbirths_​per_​100k
Michael266,3803,134
Lloyd7,87492
Olivia222
PAF8,163,949
namepopulationbirths_​per_​100k
Olivia16,292199
Michael91011
ILM7,845,252
namepopulationbirths_​per_​100k
Michael251,7413,208
Lloyd7,20791
ILF7,333,624
namepopulationbirths_​per_​100k
Olivia16,450224
Michael98917
OHM7,324,810
namepopulationbirths_​per_​100k
Michael225,3023,075
Lloyd6,75592
OHF6,876,716
namepopulationbirths_​per_​100k
Olivia16,787244
Michael96918
MIM6,036,661
namepopulationbirths_​per_​100k
Michael194,8513,227
Lloyd6,130101
MIF5,606,794
namepopulationbirths_​per_​100k
Olivia13,575242
Michael75517
FLM4,910,950
namepopulationbirths_​per_​100k
Michael138,8822,828
Lloyd2,87658
FLF4,366,273
namepopulationbirths_​per_​100k
Olivia15,208348
Michael69118
NCM4,326,810
namepopulationbirths_​per_​100k
Michael92,4142,135
Lloyd4,367100
NJM4,315,822
namepopulationbirths_​per_​100k
Michael165,3853,832
Lloyd1,72041
GAM4,214,825
namepopulationbirths_​per_​100k
Michael87,6842,080
Lloyd2,50859
NCF4,113,425
namepopulationbirths_​per_​100k
Olivia10,671259
Michael41619
[
  {
    "state": "CA",
    "gender": "M",
    "total_cohort_population": 15011405,
    "names": [
      {
        "name": "Michael",
        "population": 420127,
        "births_per_100k": 2798
      },
      {
        "name": "Lloyd",
        "population": 8380,
        "births_per_100k": 55
      }
    ]
  },
  {
    "state": "CA",
    "gender": "F",
    "total_cohort_population": 13799158,
    "names": [
      {
        "name": "Olivia",
        "population": 33794,
        "births_per_100k": 244
      },
      {
        "name": "Michael",
        "population": 2428,
        "births_per_100k": 19
      }
    ]
  },
  {
    "state": "NY",
    "gender": "M",
    "total_cohort_population": 12203961,
    "names": [
      {
        "name": "Michael",
        "population": 430732,
        "births_per_100k": 3529
      },
      {
        "name": "Lloyd",
        "population": 7568,
        "births_per_100k": 62
      },
      {
        "name": "Olivia",
        "population": 5,
        "births_per_100k": 0
      }
    ]
  },
  {
    "state": "NY",
    "gender": "F",
    "total_cohort_population": 11490175,
    "names": [
      {
        "name": "Olivia",
        "population": 23219,
        "births_per_100k": 202
      },
      {
        "name": "Michael",
        "population": 1761,
        "births_per_100k": 16
      }
    ]
  },
  {
    "state": "TX",
    "gender": "M",
    "total_cohort_population": 11126246,
    "names": [
      {
        "name": "Michael",
        "population": 223900,
        "births_per_100k": 2012
      },
      {
        "name": "Lloyd",
        "population": 8461,
        "births_per_100k": 76
      },
      {
        "name": "Olivia",
        "population": 6,
        "births_per_100k": 1
      }
    ]
  },
  {
    "state": "TX",
    "gender": "F",
    "total_cohort_population": 10341113,
    "names": [
      {
        "name": "Olivia",
        "population": 25746,
        "births_per_100k": 248
      },
      {
        "name": "Michael",
        "population": 1420,
        "births_per_100k": 16
      },
      {
        "name": "Lloyd",
        "population": 15,
        "births_per_100k": 1
      }
    ]
  },
  {
    "state": "PA",
    "gender": "M",
    "total_cohort_population": 8497961,
    "names": [
      {
        "name": "Michael",
        "population": 266380,
        "births_per_100k": 3134
      },
      {
        "name": "Lloyd",
        "population": 7874,
        "births_per_100k": 92
      },
      {
        "name": "Olivia",
        "population": 22,
        "births_per_100k": 2
      }
    ]
  },
  {
    "state": "PA",
    "gender": "F",
    "total_cohort_population": 8163949,
    "names": [
      {
        "name": "Olivia",
        "population": 16292,
        "births_per_100k": 199
      },
      {
        "name": "Michael",
        "population": 910,
        "births_per_100k": 11
      }
    ]
  },
  {
    "state": "IL",
    "gender": "M",
    "total_cohort_population": 7845252,
    "names": [
      {
        "name": "Michael",
        "population": 251741,
        "births_per_100k": 3208
      },
      {
        "name": "Lloyd",
        "population": 7207,
        "births_per_100k": 91
      }
    ]
  },
  {
    "state": "IL",
    "gender": "F",
    "total_cohort_population": 7333624,
    "names": [
      {
        "name": "Olivia",
        "population": 16450,
        "births_per_100k": 224
      },
      {
        "name": "Michael",
        "population": 989,
        "births_per_100k": 17
      }
    ]
  },
  {
    "state": "OH",
    "gender": "M",
    "total_cohort_population": 7324810,
    "names": [
      {
        "name": "Michael",
        "population": 225302,
        "births_per_100k": 3075
      },
      {
        "name": "Lloyd",
        "population": 6755,
        "births_per_100k": 92
      }
    ]
  },
  {
    "state": "OH",
    "gender": "F",
    "total_cohort_population": 6876716,
    "names": [
      {
        "name": "Olivia",
        "population": 16787,
        "births_per_100k": 244
      },
      {
        "name": "Michael",
        "population": 969,
        "births_per_100k": 18
      }
    ]
  },
  {
    "state": "MI",
    "gender": "M",
    "total_cohort_population": 6036661,
    "names": [
      {
        "name": "Michael",
        "population": 194851,
        "births_per_100k": 3227
      },
      {
        "name": "Lloyd",
        "population": 6130,
        "births_per_100k": 101
      }
    ]
  },
  {
    "state": "MI",
    "gender": "F",
    "total_cohort_population": 5606794,
    "names": [
      {
        "name": "Olivia",
        "population": 13575,
        "births_per_100k": 242
      },
      {
        "name": "Michael",
        "population": 755,
        "births_per_100k": 17
      }
    ]
  },
  {
    "state": "FL",
    "gender": "M",
    "total_cohort_population": 4910950,
    "names": [
      {
        "name": "Michael",
        "population": 138882,
        "births_per_100k": 2828
      },
      {
        "name": "Lloyd",
        "population": 2876,
        "births_per_100k": 58
      }
    ]
  },
  {
    "state": "FL",
    "gender": "F",
    "total_cohort_population": 4366273,
    "names": [
      {
        "name": "Olivia",
        "population": 15208,
        "births_per_100k": 348
      },
      {
        "name": "Michael",
        "population": 691,
        "births_per_100k": 18
      }
    ]
  },
  {
    "state": "NC",
    "gender": "M",
    "total_cohort_population": 4326810,
    "names": [
      {
        "name": "Michael",
        "population": 92414,
        "births_per_100k": 2135
      },
      {
        "name": "Lloyd",
        "population": 4367,
        "births_per_100k": 100
      }
    ]
  },
  {
    "state": "NJ",
    "gender": "M",
    "total_cohort_population": 4315822,
    "names": [
      {
        "name": "Michael",
        "population": 165385,
        "births_per_100k": 3832
      },
      {
        "name": "Lloyd",
        "population": 1720,
        "births_per_100k": 41
      }
    ]
  },
  {
    "state": "GA",
    "gender": "M",
    "total_cohort_population": 4214825,
    "names": [
      {
        "name": "Michael",
        "population": 87684,
        "births_per_100k": 2080
      },
      {
        "name": "Lloyd",
        "population": 2508,
        "births_per_100k": 59
      }
    ]
  },
  {
    "state": "NC",
    "gender": "F",
    "total_cohort_population": 4113425,
    "names": [
      {
        "name": "Olivia",
        "population": 10671,
        "births_per_100k": 259
      },
      {
        "name": "Michael",
        "population": 416,
        "births_per_100k": 19
      }
    ]
  }
]
WITH __stage0 AS (
  SELECT
    group_set,
    (floor(base.year/10))*10 as decade__0,
    base.state as state__0,
    base.gender as gender__0,
    CASE WHEN group_set=0 THEN
      COALESCE(SUM(base.number),0)
      END as cohort_population__0,
    CASE WHEN group_set=1 THEN
      base.name
      END as name__1,
    CASE WHEN group_set=1 THEN
      COALESCE(SUM(base.number),0)
      END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,6
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
      name__1 as name, 
      population__1 as population
      ) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3
  ORDER BY 4 desc
)
, __stage2 AS (
  SELECT
    group_set,
    names.state as state__0,
    names.gender as gender__0,
    CASE WHEN group_set=0 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END as total_cohort_population__0,
    CASE WHEN group_set=1 THEN
      concat(by_name_0.name,'')
      END as name__1,
    CASE WHEN group_set=1 THEN
      COALESCE(SUM(by_name_0.population),0)
      END as population__1,
    floor((CASE WHEN group_set=1 THEN
      COALESCE(SUM(by_name_0.population),0)
      END/CASE WHEN group_set=1 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END)*100000) as births_per_100k__1
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1 as x) as names
  LEFT JOIN UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  WHERE (group_set NOT IN (1) OR (group_set IN (1) AND ((concat(by_name_0.name,''))='Michael')or(((concat(by_name_0.name,''))='Lloyd')or((concat(by_name_0.name,''))='Olivia'))))
  GROUP BY 1,2,3,5
)
SELECT
  state__0 as state,
  gender__0 as gender,
  ANY_VALUE(CASE WHEN group_set=0 THEN total_cohort_population__0 END) as total_cohort_population,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
    name__1 as name, 
    population__1 as population, 
    births_per_100k__1 as births_per_100k
    ) END IGNORE NULLS  ORDER BY  population__1 desc ) as names
FROM __stage2
GROUP BY 1,2
ORDER BY 3 desc

Dashboard 

query: names -> name_dashboard { where: name ~ 'Mich%' }
QUERY RESULTS
nametotal_​populationstate_​mapdecade_​graph
Michael4,396,482
1,5002,0002,5003,0003,500births_per_100k
1,9201,9401,9601,9802,0002,020decade02,0004,000births_per_100kFemaleMalebaby_gender
Michelle813,655
4006008001,000births_per_100k
1,9201,9401,9601,9802,0002,020decade05001,0001,5002,000births_per_100kFemaleMalebaby_gender
Michele221,811
100200300400births_per_100k
1,9201,9401,9601,9802,0002,020decade0200400births_per_100kFemaleMalebaby_gender
Micheal150,977
50100150200births_per_100k
1,9201,9401,9601,9802,0002,020decade0100200births_per_100kFemaleMalebaby_gender
Michaela48,892
50100150200births_per_100k
1,9401,9601,9802,0002,020decade050100150births_per_100kFemaleMalebaby_gender
Michel7,062
10203040births_per_100k
1,9201,9401,9601,9802,0002,020decade02468births_per_100kFemaleMalebaby_gender
Michell4,377
5101520births_per_100k
1,9601,9802,000decade0510births_per_100kFemaleMalebaby_gender
Michal2,402
02468births_per_100k
1,9401,9601,9802,0002,020decade05101520births_per_100kFemaleMalebaby_gender
Michale954
1234births_per_100k
1,9601,9802,000decade0123births_per_100kMalebaby_gender
Michelina909
02468births_per_100k
1,9201,9401,9601,9802,0002,020decade0510births_per_100kFemalebaby_gender
Michaella711
123456births_per_100k
1,9601,9802,0002,020decade01234births_per_100kFemalebaby_gender
Michela621
24681012births_per_100k
1,9401,9601,9802,0002,020decade024births_per_100kFemalebaby_gender
Michaelangelo579
0123births_per_100k
1,9701,9801,9902,0002,0102,020decade0.00.51.01.52.0births_per_100kMalebaby_gender
Michaelene477
01234births_per_100k
1,9301,9401,9501,9601,9701,980decade01234births_per_100kFemalebaby_gender
Michala465
02468births_per_100k
1,9701,9801,9902,000decade024births_per_100kFemalebaby_gender
[
  {
    "name": "Michael",
    "total_population": 4396482,
    "state_map": [
      {
        "state": "RI",
        "births_per_100k": 3689
      },
      {
        "state": "DE",
        "births_per_100k": 3312
      },
      {
        "state": "AK",
        "births_per_100k": 3309
      },
      {
        "state": "VT",
        "births_per_100k": 3026
      },
      {
        "state": "NH",
        "births_per_100k": 2795
      },
      {
        "state": "ME",
        "births_per_100k": 2792
      },
      {
        "state": "WY",
        "births_per_100k": 2650
      },
      {
        "state": "HI",
        "births_per_100k": 2518
      },
      {
        "state": "CT",
        "births_per_100k": 2481
      },
      {
        "state": "NV",
        "births_per_100k": 2406
      },
      {
        "state": "DC",
        "births_per_100k": 2343
      },
      {
        "state": "MT",
        "births_per_100k": 2281
      },
      {
        "state": "MA",
        "births_per_100k": 2234
      },
      {
        "state": "NJ",
        "births_per_100k": 2225
      },
      {
        "state": "SD",
        "births_per_100k": 2218
      },
      {
        "state": "ND",
        "births_per_100k": 1968
      },
      {
        "state": "MD",
        "births_per_100k": 1960
      },
      {
        "state": "ID",
        "births_per_100k": 1950
      },
      {
        "state": "WI",
        "births_per_100k": 1911
      },
      {
        "state": "NE",
        "births_per_100k": 1909
      },
      {
        "state": "OR",
        "births_per_100k": 1862
      },
      {
        "state": "IL",
        "births_per_100k": 1849
      },
      {
        "state": "MI",
        "births_per_100k": 1832
      },
      {
        "state": "NY",
        "births_per_100k": 1814
      },
      {
        "state": "WV",
        "births_per_100k": 1792
      },
      {
        "state": "IA",
        "births_per_100k": 1766
      },
      {
        "state": "OH",
        "births_per_100k": 1758
      },
      {
        "state": "CO",
        "births_per_100k": 1735
      },
      {
        "state": "NM",
        "births_per_100k": 1728
      },
      {
        "state": "MN",
        "births_per_100k": 1695
      },
      {
        "state": "IN",
        "births_per_100k": 1684
      },
      {
        "state": "WA",
        "births_per_100k": 1670
      },
      {
        "state": "MO",
        "births_per_100k": 1654
      },
      {
        "state": "VA",
        "births_per_100k": 1650
      },
      {
        "state": "PA",
        "births_per_100k": 1606
      },
      {
        "state": "AZ",
        "births_per_100k": 1599
      },
      {
        "state": "KS",
        "births_per_100k": 1598
      },
      {
        "state": "UT",
        "births_per_100k": 1597
      },
      {
        "state": "FL",
        "births_per_100k": 1555
      },
      {
        "state": "OK",
        "births_per_100k": 1539
      },
      {
        "state": "LA",
        "births_per_100k": 1429
      },
      {
        "state": "NC",
        "births_per_100k": 1398
      },
      {
        "state": "CA",
        "births_per_100k": 1393
      },
      {
        "state": "AR",
        "births_per_100k": 1380
      },
      {
        "state": "TN",
        "births_per_100k": 1364
      },
      {
        "state": "SC",
        "births_per_100k": 1363
      },
      {
        "state": "KY",
        "births_per_100k": 1345
      },
      {
        "state": "AL",
        "births_per_100k": 1241
      },
      {
        "state": "MS",
        "births_per_100k": 1212
      },
      {
        "state": "GA",
        "births_per_100k": 1140
      },
      {
        "state": "TX",
        "births_per_100k": 1024
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 4562,
        "baby_gender": "Male"
      },
      {
        "decade": 1960,
        "births_per_100k": 4510,
        "baby_gender": "Male"
      },
      {
        "decade": 1950,
        "births_per_100k": 4301,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
        "births_per_100k": 3848,
        "baby_gender": "Male"
      },
      {
        "decade": 1990,
        "births_per_100k": 2587,
        "baby_gender": "Male"
      },
      {
        "decade": 1940,
        "births_per_100k": 2367,
        "baby_gender": "Male"
      },
      {
        "decade": 2000,
        "births_per_100k": 1409,
        "baby_gender": "Male"
      },
      {
        "decade": 2010,
        "births_per_100k": 884,
        "baby_gender": "Male"
      },
      {
        "decade": 2020,
        "births_per_100k": 632,
        "baby_gender": "Male"
      },
      {
        "decade": 1930,
        "births_per_100k": 564,
        "baby_gender": "Male"
      },
      {
        "decade": 1910,
        "births_per_100k": 501,
        "baby_gender": "Male"
      },
      {
        "decade": 1920,
        "births_per_100k": 366,
        "baby_gender": "Male"
      },
      {
        "decade": 1970,
        "births_per_100k": 35,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 32,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 19,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 10,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 10,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 8,
        "baby_gender": "Female"
      },
      {
        "decade": 1930,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1920,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 0,
        "baby_gender": "Female"
      },
      {
        "decade": 1910,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  },
  {
    "name": "Michelle",
    "total_population": 813655,
    "state_map": [
      {
        "state": "VT",
        "births_per_100k": 1137
      },
      {
        "state": "AK",
        "births_per_100k": 1017
      },
      {
        "state": "WY",
        "births_per_100k": 992
      },
      {
        "state": "HI",
        "births_per_100k": 992
      },
      {
        "state": "DE",
        "births_per_100k": 920
      },
      {
        "state": "RI",
        "births_per_100k": 903
      },
      {
        "state": "ME",
        "births_per_100k": 889
      },
      {
        "state": "NH",
        "births_per_100k": 878
      },
      {
        "state": "NE",
        "births_per_100k": 834
      },
      {
        "state": "SD",
        "births_per_100k": 834
      },
      {
        "state": "ND",
        "births_per_100k": 825
      },
      {
        "state": "NM",
        "births_per_100k": 773
      },
      {
        "state": "MT",
        "births_per_100k": 768
      },
      {
        "state": "CO",
        "births_per_100k": 762
      },
      {
        "state": "NV",
        "births_per_100k": 756
      },
      {
        "state": "IA",
        "births_per_100k": 750
      },
      {
        "state": "CT",
        "births_per_100k": 746
      },
      {
        "state": "KS",
        "births_per_100k": 740
      },
      {
        "state": "OR",
        "births_per_100k": 735
      },
      {
        "state": "DC",
        "births_per_100k": 725
      },
      {
        "state": "UT",
        "births_per_100k": 721
      },
      {
        "state": "WA",
        "births_per_100k": 704
      },
      {
        "state": "ID",
        "births_per_100k": 695
      },
      {
        "state": "AZ",
        "births_per_100k": 683
      },
      {
        "state": "MO",
        "births_per_100k": 659
      },
      {
        "state": "MA",
        "births_per_100k": 648
      },
      {
        "state": "IN",
        "births_per_100k": 607
      },
      {
        "state": "MN",
        "births_per_100k": 589
      },
      {
        "state": "MD",
        "births_per_100k": 578
      },
      {
        "state": "MI",
        "births_per_100k": 560
      },
      {
        "state": "WI",
        "births_per_100k": 527
      },
      {
        "state": "OH",
        "births_per_100k": 521
      },
      {
        "state": "OK",
        "births_per_100k": 502
      },
      {
        "state": "LA",
        "births_per_100k": 502
      },
      {
        "state": "NJ",
        "births_per_100k": 501
      },
      {
        "state": "VA",
        "births_per_100k": 486
      },
      {
        "state": "IL",
        "births_per_100k": 466
      },
      {
        "state": "FL",
        "births_per_100k": 458
      },
      {
        "state": "PA",
        "births_per_100k": 456
      },
      {
        "state": "CA",
        "births_per_100k": 446
      },
      {
        "state": "WV",
        "births_per_100k": 438
      },
      {
        "state": "AR",
        "births_per_100k": 417
      },
      {
        "state": "NY",
        "births_per_100k": 414
      },
      {
        "state": "SC",
        "births_per_100k": 402
      },
      {
        "state": "KY",
        "births_per_100k": 363
      },
      {
        "state": "TN",
        "births_per_100k": 324
      },
      {
        "state": "GA",
        "births_per_100k": 314
      },
      {
        "state": "TX",
        "births_per_100k": 308
      },
      {
        "state": "MS",
        "births_per_100k": 296
      },
      {
        "state": "NC",
        "births_per_100k": 283
      },
      {
        "state": "AL",
        "births_per_100k": 277
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 1814,
        "baby_gender": "Female"
      },
      {
        "decade": 1960,
        "births_per_100k": 1230,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 1018,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 559,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 283,
        "baby_gender": "Female"
      },
      {
        "decade": 1950,
        "births_per_100k": 258,
        "baby_gender": "Female"
      },
      {
        "decade": 2010,
        "births_per_100k": 120,
        "baby_gender": "Female"
      },
      {
        "decade": 2020,
        "births_per_100k": 65,
        "baby_gender": "Female"
      },
      {
        "decade": 1940,
        "births_per_100k": 40,
        "baby_gender": "Female"
      },
      {
        "decade": 1970,
        "births_per_100k": 5,
        "baby_gender": "Male"
      },
      {
        "decade": 1980,
        "births_per_100k": 4,
        "baby_gender": "Male"
      },
      {
        "decade": 1930,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 3,
        "baby_gender": "Male"
      },
      {
        "decade": 1960,
        "births_per_100k": 2,
        "baby_gender": "Male"
      },
      {
        "decade": 1920,
        "births_per_100k": 1,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 0,
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        "state": "OK",
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      {
        "state": "FL",
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        "state": "TX",
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        "decade": 1960,
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        "baby_gender": "Male"
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        "decade": 1980,
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        "decade": 2010,
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        "baby_gender": "Female"
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        "decade": 1920,
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        "baby_gender": "Female"
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      {
        "decade": 1990,
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        "baby_gender": "Male"
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      {
        "decade": 1950,
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        "baby_gender": "Male"
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      {
        "decade": 1930,
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        "baby_gender": "Male"
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        "state": "NV",
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        "state": "AZ",
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      {
        "state": "IL",
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        "state": "OH",
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        "state": "TX",
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        "state": "NH",
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        "state": "DE",
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        "state": "CA",
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        "state": "NY",
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        "state": "RI",
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      {
        "state": "NJ",
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      {
        "state": "MA",
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      },
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        "decade": 2010,
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      {
        "decade": 2020,
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        "decade": 1930,
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        "decade": 2000,
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      {
        "decade": 1950,
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        "decade": 1980,
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        "state": "IL",
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      {
        "state": "NY",
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      },
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    ],
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        "state": "KY",
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        "state": "WA",
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        "state": "TN",
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      {
        "state": "CO",
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        "state": "TX",
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      {
        "state": "MI",
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        "state": "KS",
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        "state": "CT",
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      },
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        "state": "IL",
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        "state": "NC",
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        "state": "MD",
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      },
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        "state": "NJ",
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      {
        "state": "WI",
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      },
      {
        "state": "MN",
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      },
      {
        "state": "PA",
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      },
      {
        "state": "AZ",
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      }
    ],
    "decade_graph": [
      {
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      {
        "decade": 1960,
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        "decade": 1980,
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        "baby_gender": "Male"
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      {
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      {
        "decade": 2020,
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        "decade": 1940,
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      {
        "decade": 2010,
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      {
        "state": "DC",
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      },
      {
        "state": "AZ",
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      {
        "state": "CA",
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      },
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        "state": "FL",
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      {
        "state": "WA",
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      {
        "state": "TX",
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      },
      {
        "state": "WI",
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      },
      {
        "state": "NC",
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      },
      {
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      },
      {
        "state": "MN",
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      {
        "state": "OR",
        "births_per_100k": 3
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      {
        "state": "MA",
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      },
      {
        "state": "VA",
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      {
        "state": "IA",
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      {
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        "decade": 1950,
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      {
        "state": "NY",
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        "state": "IA",
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      {
        "state": "CO",
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      },
      {
        "state": "TX",
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      {
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      {
        "state": "FL",
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      {
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      {
        "state": "AL",
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      },
      {
        "state": "MI",
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    ],
    "decade_graph": [
      {
        "decade": 2020,
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      {
        "decade": 2010,
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        "decade": 2000,
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      {
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      {
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        "state": "NY",
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      },
      {
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      },
      {
        "state": "TN",
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    ]
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  {
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        "state": "MI",
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      },
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        "state": "CA",
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    ],
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  },
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      {
        "state": "AZ",
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      {
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      },
      {
        "state": "CA",
        "births_per_100k": 1
      },
      {
        "state": "NY",
        "births_per_100k": 1
      },
      {
        "state": "WA",
        "births_per_100k": 1
      },
      {
        "state": "IN",
        "births_per_100k": 1
      },
      {
        "state": "FL",
        "births_per_100k": 0
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 5,
        "baby_gender": "Female"
      },
      {
        "decade": 1980,
        "births_per_100k": 4,
        "baby_gender": "Female"
      },
      {
        "decade": 1990,
        "births_per_100k": 3,
        "baby_gender": "Female"
      },
      {
        "decade": 2000,
        "births_per_100k": 0,
        "baby_gender": "Female"
      }
    ]
  }
]
WITH __stage0 AS (
  SELECT
    group_set,
    (floor(base.year/10))*10 as decade__0,
    base.state as state__0,
    CASE WHEN base.gender='M' THEN 'Male' ELSE 'Female' END as baby_gender__0,
    base.gender as gender__0,
    CASE WHEN group_set=0 THEN
      COALESCE(SUM(base.number),0)
      END as cohort_population__0,
    CASE WHEN group_set=1 THEN
      base.name
      END as name__1,
    CASE WHEN group_set=1 THEN
      COALESCE(SUM(base.number),0)
      END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_current` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,5,7
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    baby_gender__0 as baby_gender,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
      name__1 as name, 
      population__1 as population
      ) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3,4
  ORDER BY 5 desc
)
, __stage2 AS (
  SELECT
    group_set,
    concat(by_name_0.name,'') as name__0,
    CASE WHEN group_set=0 THEN
      COALESCE(SUM(by_name_0.population),0)
      END as total_population__0,
    CASE WHEN group_set=1 THEN
      names.state
      END as state__1,
    floor((CASE WHEN group_set=1 THEN
      COALESCE(SUM(by_name_0.population),0)
      END/CASE WHEN group_set=1 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END)*100000) as births_per_100k__1,
    CASE WHEN group_set=2 THEN
      names.decade
      END as decade__2,
    floor((CASE WHEN group_set=2 THEN
      COALESCE(SUM(by_name_0.population),0)
      END/CASE WHEN group_set=2 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END)*100000) as births_per_100k__2,
    CASE WHEN group_set=2 THEN
      names.baby_gender
      END as baby_gender__2
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1 as x) as names
  LEFT JOIN UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  WHERE (concat(by_name_0.name,'')) LIKE 'Mich%'
  GROUP BY 1,2,4,6,8
)
SELECT
  name__0 as name,
  ANY_VALUE(CASE WHEN group_set=0 THEN total_population__0 END) as total_population,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
    state__1 as state, 
    births_per_100k__1 as births_per_100k
    ) END IGNORE NULLS  ORDER BY  births_per_100k__1 desc ) as state_map,
  ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(
    decade__2 as decade, 
    births_per_100k__2 as births_per_100k, 
    baby_gender__2 as baby_gender
    ) END IGNORE NULLS  ORDER BY  births_per_100k__2 desc ) as decade_graph
FROM __stage2
GROUP BY 1
ORDER BY 2 desc
LIMIT 15

Iconic Names by State and Gender

Calculate the births per 100K for a name in general and a name within a state. Compute and sort by a ratio to figure out relative popularity.

query: names { where: decade < 1970 } -> {
  group_by: name
  group_by: gender
  aggregate: births_per_100k
  nest: by_state is {
    group_by: state
    aggregate: births_per_100k
  }
} -> {
  where: births_per_100k > 50
  group_by: by_state.state
  nest: by_gender is {
    group_by: gender
    nest: by_name is {
      top: 15
      order_by: popularity desc
      group_by: name
      aggregate: popularity is (by_state.births_per_100k - births_per_100k) / births_per_100k
    }
  }
}
QUERY RESULTS
stateby_​gender
AK
gender
F
by_name
namepopularity
Daisy5.229
Shannon2.506
Lucy2.389
Sophie1.918
Agnes1.852
Tina1.846
Deanna1.841
Katherine1.735
Colleen1.652
Kristin1.567
Leslie1.497
Teresa1.467
Heather1.424
Heidi1.364
Valerie1.361
gender
M
by_name
namepopularity
Shawn3.153
Jason2.708
Troy1.667
Tracy1.623
Sean1.605
Peter1.521
Patrick1.442
Karl1.405
Matthew1.311
Dwayne1.281
Darren1.237
Eric1.203
Mike1.148
Andrew1.14
Micheal1.128
AL
gender
F
by_name
namepopularity
Annie4.751
Willie3.772
Mattie3.689
Johnnie3.338
Rosie3.034
Shelia2.987
Katie2.964
Fannie2.84
Lillie2.705
Essie2.627
Lula2.536
Minnie2.412
Hattie2.318
Mamie2.301
Susie2.299
gender
M
by_name
namepopularity
Willie4.273
Charlie3.119
Roosevelt2.824
Earnest2.691
Jessie2.576
Johnnie2.554
Rickey2.33
Eddie2.284
Jimmie2.24
Horace2.129
Otis2.1
Jimmy2.036
Johnny1.912
Billy1.828
Bennie1.793
AR
gender
F
by_name
namepopularity
Rosie3.23
Bobbie2.79
Erma2.773
Patsy2.558
Billie2.486
Ruby2.28
Velma2.245
Opal2.216
Ollie2.196
Geneva2.163
Willie2.117
Essie1.941
Jessie1.883
Shelia1.848
Lola1.838
gender
M
by_name
namepopularity
Rickey3.636
Billy3.309
Bobby3.242
Jimmy2.891
Jessie2.455
Earnest2.426
Freddie2.321
Tommy2.281
Jimmie2.202
Otis2.15
Jackie2.138
Bennie1.897
Roosevelt1.882
Luther1.821
Cecil1.748
AZ
gender
F
by_name
namepopularity
Carmen5.642
Cecilia3.719
Maria3.649
Lupe3.222
Lydia3.197
Yolanda3
Ramona2.556
Dora1.956
Guadalupe1.911
Susie1.61
Alicia1.606
Veronica1.504
Christina1.492
Rosie1.471
Monica1.394
gender
M
by_name
namepopularity
Manuel8.991
Ruben5.639
Francisco5
Carlos4.418
Jesus4.324
Jose3.604
Gilbert3.48
Antonio3.476
Raul3.145
Alex3.078
Hector2.962
Miguel2.704
Juan2.457
Luis2.365
Woodrow2.058
CA
gender
F
by_name
namepopularity
Christina1.358
Carmen1.347
Yolanda1.257
Teri1.06
Maria1.009
Pat0.907
Cecilia0.877
Leslie0.848
Julie0.837
Victoria0.829
Monica0.78
Ramona0.764
Alicia0.727
Lydia0.724
Shannon0.718
gender
M
by_name
namepopularity
Manuel2.327
Ruben2
Carlos1.468
Mario1.231
Mike1.196
Jason1.069
Bob1.036
Lance0.982
Gilbert0.967
Jim0.965
Dave0.91
Ricardo0.873
Jose0.86
Eric0.829
Chris0.824
CO
gender
F
by_name
namepopularity
Kristi1.962
Shelly1.39
Olive1.269
Bernadette1.211
Ramona1.194
Becky1.101
Nicole1.091
Heather1.071
Della1.053
Heidi1.03
Erma1.015
Dianna1.015
Karla0.968
Kelli0.964
Shannon0.918
gender
M
by_name
namepopularity
Kent1.944
Manuel1.645
Kirk1.606
Woodrow1.558
Gilbert1.374
Kelly1.333
Brett1.288
Bob1.205
Shawn1.119
Delbert1.109
Rick1.104
Mike1.074
Jason1.028
Dave1
Joe0.985
CT
gender
F
by_name
namepopularity
Sophie2.656
Doreen2.646
Claire1.772
Lynn1.664
Gail1.564
Antoinette1.513
Kristen1.509
Maureen1.477
Maryann1.461
Laurie1.408
Marion1.343
Jennie1.258
Heather1.188
Anne1.143
Heidi1.101
gender
M
by_name
namepopularity
Peter2.449
Nicholas1.984
Salvatore1.9
Jonathan1.737
Angelo1.643
Francis1.475
Alexander1.353
Vincent1.269
Marc1.171
Sean1.168
Frederick1.153
Anthony1.022
Louis0.966
Joseph0.908
Theodore0.841
DC
gender
F
by_name
namepopularity
Cassandra2.019
Crystal2.017
Yvette1.867
Tonya1.833
Tanya1.719
Gwendolyn1.551
Leslie1.4
Jacqueline1.365
Delores1.331
Laverne1.327
Renee1.319
Vanessa1.316
Nicole1.291
Monica1.266
Yvonne1.253
gender
M
by_name
namepopularity
Tyrone5.263
Reginald3.785
Derrick2.627
Maurice1.908
Ricardo1.764
Darryl1.693
Dwayne1.688
Derek1.685
Nathaniel1.417
Jonathan1.331
Sean1.261
Eric1.219
Clifton1.167
Bernard1.093
Christopher1
DE
gender
F
by_name
namepopularity
Crystal3.397
Sophie2.869
Mabel2.578
Stella2.339
Hilda2.325
Heather2.129
Elsie2.097
Pearl1.839
Nicole1.836
Agnes1.698
Holly1.589
Kristin1.582
Dawn1.519
Olive1.519
Blanche1.449
gender
M
by_name
namepopularity
Woodrow3.981
Francis2.381
Sean1.479
Matthew1.257
Tyrone1.246
Harry1.212
Roland1.168
Christopher1.167
Franklin1.139
Dwayne1.063
Horace1.048
Willard1.047
Wayne1.04
Bryan1.034
Jonathan0.907
FL
gender
F
by_name
namepopularity
Cassandra1.704
Gwendolyn1.525
Jacquelyn1.323
Rosa1.043
Essie0.98
Belinda0.967
Ernestine0.909
Annie0.866
Iris0.839
Tina0.829
Daisy0.829
Johnnie0.824
Angela0.821
Willie0.772
Inez0.743
gender
M
by_name
namepopularity
Nathaniel2.236
Freddie1.744
Willie1.716
Johnnie1.438
Reginald1.367
Roosevelt1.235
Horace1.177
Eddie1.096
Charlie0.902
Derrick0.896
Jimmie0.845
Johnny0.843
Dwayne0.766
Tyrone0.754
Earnest0.75
GA
gender
F
by_name
namepopularity
Annie3.997
Mattie3.763
Willie3.178
Essie3.098
Mamie3.027
Johnnie2.73
Susie2.494
Fannie2.444
Lillie2.409
Hattie2.282
Katie2.25
Sara2.183
Rosa2.173
Daisy2.071
Lucile2.068
gender
M
by_name
namepopularity
Charlie4.636
Willie4.57
Horace3.081
Otis2.733
Bennie2.483
Johnnie2.438
Eddie2.391
Freddie2.115
Nathaniel2.083
Earnest2.074
Johnny2.044
Marion1.813
Roosevelt1.804
Hugh1.772
Hubert1.769
HI
gender
F
by_name
namepopularity
Kiyoko6.789
Yoshiko6.393
Masako5.984
Fumiko5.911
Mitsue5.868
Shizue4.775
Roxanne4.723
Bernadette4.579
Yoshie4.027
Shizuko3.975
Kikue3.966
Iris3.911
Mitsuko3.888
Violet3.632
Doreen3.415
gender
M
by_name
namepopularity
Toshio7.902
Yoshio7.885
Kiyoshi6.985
Kazuo6.426
Lance6.089
Minoru5.981
Derek5.833
Takeo4.941
Jason4.792
Clayton4.373
Shigeo4.311
Nathan3.968
Guy3.588
Wendell3.208
Sean3.092
IA
gender
F
by_name
namepopularity
Becky1.927
Karla1.921
Shelly1.848
Maxine1.826
Kristi1.577
Julie1.523
Jodi1.443
Sheryl1.403
Marcella1.339
Connie1.327
Tami1.302
Erma1.288
Jody1.269
Kristine1.227
Roxanne1.108
gender
M
by_name
namepopularity
Lyle3.762
Duane2.508
Kent2.113
Dean2.096
Woodrow1.788
Darin1.774
Max1.685
Bradley1.61
Rick1.584
Delbert1.564
Virgil1.487
Dale1.471
Lynn1.45
Jon1.441
Rex1.439
ID
gender
F
by_name
namepopularity
Olive3.808
Karla3.206
Heather2.929
Shannon2.812
Shelley2.73
Kristi2.558
Maxine2.53
Becky2.468
Blanche2.359
Tamara2.342
Robyn2.17
Tami2.159
Jody2.119
Opal2.093
Shelly2.048
gender
M
by_name
namepopularity
Kelly5.944
Brent5.598
Darin5.453
Lynn5.2
Woodrow4.231
Kim3.879
Kent3.789
Lyle3.413
Rex3.368
Max2.973
Tracy2.803
Brett2.493
Brad2.462
Delbert2.4
Rick2.264
IL
gender
F
by_name
namepopularity
Laverne1.273
Dolores0.667
Rosemary0.612
Lorraine0.606
Genevieve0.604
Marcella0.581
Pat0.556
Marianne0.5
Marilyn0.459
Judith0.416
Loretta0.383
Arlene0.37
Eileen0.354
Bernice0.348
Kristine0.33
gender
M
by_name
namepopularity
Jerome0.604
Derrick0.537
Tyrone0.474
Maurice0.429
Bradley0.366
Tom0.336
Lyle0.333
Brad0.295
Chester0.273
Patrick0.264
Kurt0.256
Lawrence0.252
Bob0.25
Martin0.248
Darryl0.248
IN
gender
F
by_name
namepopularity
Tamara1.351
Patty1.193
Tina1.146
Wilma1.017
Penny0.969
Beth0.96
Marsha0.952
Olive0.942
Sue0.937
Lana0.922
Dianna0.912
Becky0.826
Tonya0.822
Marcia0.814
Rebecca0.755
gender
M
by_name
namepopularity
Max2.37
Rex1.93
Kent1.338
Rick1.28
Brent1.134
Bradley1.033
Jeffery0.967
Jeff0.954
Tim0.946
Wilbur0.905
Darrin0.885
Kerry0.879
Darin0.868
Garry0.833
Terry0.754
KS
gender
F
by_name
namepopularity
Karla2.111
Kristi1.923
Shelly1.819
Maxine1.811
Wilma1.73
Opal1.557
Deanna1.449
Connie1.414
Velma1.392
Lana1.373
Lola1.294
Dana1.259
Beulah1.233
Dianna1.221
Sheryl1.202
gender
M
by_name
namepopularity
Darin3.528
Max2.452
Rex2.281
Delbert2.2
Kelly2.167
Virgil2.013
Lyle1.984
Kent1.873
Bradley1.634
Brad1.526
Bob1.375
Brent1.329
Duane1.234
Everett1.231
Marion1.213
KY
gender
F
by_name
namepopularity
Geneva2.827
Della2.07
Opal1.969
Shelia1.797
Wilma1.759
Beulah1.616
Ruby1.472
Wanda1.425
Vickie1.357
Ada1.348
Alma1.273
Nora1.2
Myrtle1.183
Thelma1.103
Eula1.097
gender
M
by_name
namepopularity
Donnie3.343
Jackie2.255
Wendell1.792
Homer1.729
Virgil1.718
Cecil1.687
Ronnie1.646
Bobby1.596
Billy1.596
Hubert1.538
Garry1.5
Everett1.462
Danny1.368
Willard1.365
Luther1.328
LA
gender
F
by_name
namepopularity
Shelia2.582
Irma1.824
Eula1.758
Rosie1.713
Vanessa1.663
Gwendolyn1.619
Mable1.581
Lillie1.43
Hilda1.373
Bobbie1.31
Glenda1.306
Ella1.302
Willie1.284
Cassandra1.241
Paulette1.217
gender
M
by_name
namepopularity
Sidney2.385
Clifton2.25
Freddie2.231
Alvin2.071
Roosevelt1.98
Jessie1.909
Dwayne1.719
Willie1.635
Tyrone1.491
Oliver1.49
Kerry1.466
Herman1.43
Earnest1.412
Calvin1.401
Byron1.358
MA
gender
F
by_name
namepopularity
Claire5.013
Maureen3.052
Priscilla2.288
Lynne1.826
Jeannette1.779
Anne1.585
Joanne1.524
Doreen1.477
Eleanor1.475
Muriel1.418
Sophie1.393
Marion1.295
Eileen1.282
Jeanne1.277
Lorraine1.236
gender
M
by_name
namepopularity
Francis2.843
Peter2.057
Gerard2.014
Dana1.87
Roland1.642
Leo1.503
Frederick1.281
Stephen1.266
Philip1.186
Paul1.179
Jonathan1.144
Alfred1.028
Sean0.966
Arthur0.903
Joseph0.856
MD
gender
F
by_name
namepopularity
Crystal1.638
Hilda1.325
Vanessa1.306
Yvette1.217
Robin1.016
Rosalie0.887
Darlene0.885
Bernadette0.825
Dawn0.814
Terry0.803
Catherine0.801
Tracey0.761
Naomi0.75
Regina0.75
Michele0.749
gender
M
by_name
namepopularity
Tyrone2.07
Dwayne1.484
Roland1.221
Reginald1.127
Clifton1.117
Bernard1.07
Maurice0.969
Franklin0.869
Melvin0.861
Milton0.86
Vernon0.84
Jerome0.832
Harry0.803
Nelson0.732
Darrin0.731
ME
gender
F
by_name
namepopularity
Priscilla4.3
Jeannette3.632
Muriel3.313
Olive2.654
Claire2.506
Constance2.305
Pauline2.265
Madeline2.067
Laurie1.937
Rachel1.85
Debora1.667
Marguerite1.632
Kristen1.623
Heidi1.576
Tina1.529
gender
M
by_name
namepopularity
Dana7.278
Roland6.516
Woodrow4.308
Gerard3.722
Maurice3.633
Clayton2.864
Reginald2.62
Everett2.359
Troy2.133
Philip2.01
Adam1.963
Normand1.852
Marc1.714
Leo1.693
Shawn1.619
MI
gender
F
by_name
namepopularity
Sally0.76
Dawn0.74
Colleen0.652
Sue0.626
Julie0.616
Marcia0.603
Kay0.579
Genevieve0.56
Kelly0.556
Suzanne0.528
Marlene0.513
Michelle0.491
Renee0.488
Shelly0.476
Sheryl0.476
gender
M
by_name
namepopularity
Duane1.065
Gerald0.939
Kim0.862
Gordon0.855
Dave0.836
Lyle0.825
Rick0.784
Darrin0.731
Dale0.692
Fredrick0.673
Kurt0.616
Kirk0.606
Douglas0.556
Dennis0.534
Tim0.514
MN
gender
F
by_name
namepopularity
Jodi2.545
Jody2.06
Marcella2
Kristin1.881
Roxanne1.862
Kristine1.716
Kristi1.712
Colleen1.553
Harriet1.542
Julie1.472
Lorraine1.407
Heidi1.394
Shelly1.343
Delores1.324
Marlene1.256
gender
M
by_name
namepopularity
Lyle3.079
Duane2.919
Bradley2.675
Jerome1.96
Dean1.955
Jason1.889
Darin1.642
Gordon1.62
Todd1.536
Jon1.469
Troy1.43
Dale1.399
Arnold1.366
Allan1.294
Scott1.268
MO
gender
F
by_name
namepopularity
Wilma1.471
Opal1.155
Laverne1.073
Velma0.931
Marcella0.855
Lana0.824
Christy0.804
Beulah0.756
Shelly0.752
Carla0.745
Glenda0.729
Georgia0.726
Norma0.717
Erma0.712
Marsha0.644
gender
M
by_name
namepopularity
Virgil1.397
Darin1.113
Bob1.045
Jackie1.021
Everett0.923
Delbert0.909
Bill0.847
Elmer0.803
Garry0.7
Vernon0.686
Lloyd0.676
Floyd0.673
Oliver0.647
Billy0.641
Marion0.6
MS
gender
F
by_name
namepopularity
Rosie8.402
Annie5.016
Willie4.904
Bobbie4.2
Maggie4.078
Mattie4.052
Katie4.036
Lillie4
Hattie3.988
Essie3.902
Johnnie3.824
Lula3.738
Fannie3.42
Minnie3.176
Jessie3.039
gender
M
by_name
namepopularity
Willie6.282
Roosevelt6.078
Jessie5.535
Johnnie4.339
Eddie3.868
Earnest3.794
Jimmie3.744
Charlie3.734
Bennie3.569
Clifton2.85
Otis2.8
Luther2.373
Sam2.349
Freddie2.192
Johnny1.96
MT
gender
F
by_name
namepopularity
Tami2.683
Mabel2.657
Pat2.593
Shelly2.533
Jamie2.521
Teri2.418
Olive2.385
Karla2.302
Kristi2.192
Patti2.119
Genevieve2.066
Colleen1.929
Shannon1.906
Shelley1.899
Marlene1.776
gender
M
by_name
namepopularity
Kelly3
Lyle2.714
Shawn2.407
Woodrow2.288
Brett2.123
Jason1.972
Brad1.962
Darin1.925
Duane1.887
Darren1.814
Kirk1.761
Dan1.722
Tim1.676
Lance1.625
Rick1.608
NC
gender
F
by_name
namepopularity
Annie3.016
Maggie2.313
Katie2.143
Hattie2.129
Mamie1.973
Sadie1.893
Mable1.71
Lillie1.604
Shelia1.557
Rosa1.554
Fannie1.469
Hilda1.458
Rachel1.425
Crystal1.414
Daisy1.4
gender
M
by_name
namepopularity
Charlie2.713
Clifton2.2
Bobby2.021
Hubert1.969
Luther1.955
Johnnie1.688
Donnie1.687
Horace1.645
Clyde1.424
Claude1.418
Willie1.374
Johnny1.372
Ricky1.286
Jimmy1.258
Marshall1.197
ND
gender
F
by_name
namepopularity
Kristi6.365
Karla5.413
Violet4.057
Myrtle3.603
Shelly3.514
Viola3.297
Roxanne3.277
Jodi3.136
Marlene3.135
Paulette3.043
Olga2.853
Hilda2.831
Olive2.692
Leona2.642
Delores2.615
gender
M
by_name
namepopularity
Lyle6.333
Darin6.132
Troy5.985
Duane5.839
Kelly3.611
Brent3.305
Darren3.206
Bradley2.992
Lynn2.967
Jason2.819
Arnold2.554
Gordon2.53
Vernon2.526
Kim2.466
Dean2.436
NE
gender
F
by_name
namepopularity
Kristi3.192
Opal2.742
Erma2.197
Maxine2.023
Shelly1.962
Jody1.522
Tami1.492
Jodi1.409
Julie1.408
Olive1.404
Leona1.377
Wilma1.356
Kay1.306
Deanna1.243
Christy1.235
gender
M
by_name
namepopularity
Lyle4.508
Duane2.46
Darin2.377
Kent2.141
Dean1.814
Bradley1.813
Dale1.805
Delbert1.709
Rex1.649
Virgil1.628
Troy1.607
Lynn1.55
Kirk1.493
Rick1.408
Kelly1.403
NH
gender
F
by_name
namepopularity
Kristen6.698
Claire6.203
Jeannette4.912
Priscilla4.863
Heidi3.889
Blanche3.808
Muriel3.612
Olive3.385
Pauline3.313
Nicole2.745
Kristin2.597
Sophie2.508
Stella2.492
Lorraine2.175
Gertrude2.155
gender
M
by_name
namepopularity
Roland7.726
Maurice6.01
Gerard5.139
Dana4.426
Marc3.181
Leo2.687
Normand2.309
Shawn2.153
Jonathan2.136
Peter1.978
Norman1.954
Roger1.828
Jason1.611
Bruce1.494
Scott1.345
NJ
gender
F
by_name
namepopularity
Maryann2.289
Claire1.873
Eileen1.578
Antoinette1.526
Angelina1.462
Rosemarie1.418
Madeline1.28
Maureen1.267
Marianne1.214
Michele1.148
Doreen1.138
Joan1.03
Eleanor0.993
Marion0.971
Sophie0.967
gender
M
by_name
namepopularity
Nicholas2.146
Dominick1.961
Angelo1.911
Vincent1.671
Peter1.492
Alexander1.294
Salvatore1.289
Anthony1.23
Gerard1.167
Joseph1.147
Frank0.95
Louis0.931
Frederick0.803
Edward0.785
Theodore0.785
NM
gender
F
by_name
namepopularity
Cecilia6
Ramona4.694
Carmen4.421
Bernadette3.965
Maria3.782
Lucy3.595
Priscilla3.563
Flora2.969
Yolanda2.771
Dora2.67
Marcella2.645
Lydia2.421
Lupe2.222
Stella2.161
Rosie2.115
gender
M
by_name
namepopularity
Manuel10.218
Jose7.425
Antonio5.476
Gilbert5.228
Ruben5.016
Juan4.843
Carlos4.278
Joe3.835
Pedro2.887
Alex2.766
Luis2.765
Francisco2.75
Miguel2.741
Jesus2.676
Bennie2.517
NV
gender
F
by_name
namepopularity
Olga5.103
Traci4.339
Shannon4.2
Stacey4.148
Pat3.981
Toni3.165
Natalie3.15
Teri3.015
Kelli2.982
Marguerite2.947
Tina2.888
Blanche2.795
Tami2.746
Nicole2.745
Leslie2.673
gender
M
by_name
namepopularity
Jason4.333
Shawn4.178
Troy4.022
Kelly3.736
Woodrow3.654
Lance2.982
Darren2.711
Mike2.526
Brent2
Aaron1.986
Adam1.963
Darin1.962
Brett1.959
Delbert1.891
Tracy1.639
NY
gender
F
by_name
namepopularity
Antoinette1.756
Eileen1.675
Madeline1.653
Miriam1.618
Muriel1.567
Angelina1.5
Rosemarie1.463
Maureen1.355
Maryann1.158
Josephine1
Marion1
Claire0.987
Anne0.987
Joan0.915
Doreen0.908
gender
M
by_name
namepopularity
Salvatore2.222
Angelo2.054
Dominick2.02
Vincent1.963
Nicholas1.797
Gerard1.792
Peter1.687
Stuart1.21
Anthony1.184
Seymour1.111
Alexander1.035
Martin0.915
Neil0.908
Philip0.89
Louis0.849
OH
gender
F
by_name
namepopularity
Beth0.678
Patty0.651
Sue0.638
Amy0.591
Marcella0.5
Dianna0.456
Becky0.45
Tonya0.433
Kimberly0.422
Rosemary0.412
Marcia0.397
Darla0.397
Penny0.394
Sandy0.392
Marilyn0.39
gender
M
by_name
namepopularity
Rick0.776
Tim0.703
Dave0.687
Dale0.597
Wilbur0.587
Darrin0.519
Jeffrey0.499
Tom0.493
Timothy0.481
Jeffery0.475
Jeff0.424
Ted0.41
Karl0.369
Carl0.356
Dwight0.351
OK
gender
F
by_name
namepopularity
Opal4.134
Billie3.706
Shelly2.429
Velma2.196
Kristi2.192
Patsy2.154
Darla2.138
Wanda2.114
Wilma1.931
Geneva1.786
Lola1.765
Juanita1.704
Erma1.682
Kelli1.655
Ruby1.609
gender
M
by_name
namepopularity
Billy3.137
Jackie2.713
Jimmie2.543
Jimmy2.367
Tommy2.292
Bill2.243
Cecil2.148
Bobby2.11
Delbert2.036
Bob1.964
Virgil1.833
Floyd1.752
Rickey1.67
Don1.54
Ronnie1.472
OR
gender
F
by_name
namepopularity
Tami3.095
Shannon2.647
Shelley1.944
Julie1.909
Kristi1.904
Tamara1.883
Shelly1.705
Lucile1.695
Jan1.557
Becky1.477
Deanna1.449
Kelli1.436
Teri1.433
Karla1.413
Traci1.371
gender
M
by_name
namepopularity
Darin2.981
Woodrow2.75
Kelly2.569
Rick2.16
Jason2.028
Jim1.849
Bob1.786
Dan1.722
Dave1.657
Brad1.603
Delbert1.6
Jeff1.583
Mike1.561
Lyle1.524
Bradley1.512
PA
gender
F
by_name
namepopularity
Anna1.388
Dolores1.13
Maryann1.053
Eleanor0.979
Miriam0.974
Bernadette0.965
Marian0.748
Sophie0.738
Rosemarie0.716
Agnes0.711
Helen0.692
Florence0.687
Stella0.627
Joanne0.622
Marianne0.6
gender
M
by_name
namepopularity
Francis1.395
Harry1.147
Joseph1.013
Nicholas0.919
Barry0.713
Bernard0.668
Vincent0.598
Edward0.595
Andrew0.556
Samuel0.552
Theodore0.528
Alexander0.482
Albert0.463
Stanley0.452
Frank0.441
RI
gender
F
by_name
namepopularity
Claire7.051
Kristen4.245
Muriel4.164
Angelina4
Doreen3.4
Maureen3.221
Jeannette3.015
Madeline2.48
Flora2.313
Jennie2.289
Gertrude2.208
Laurie2.103
Gail2.011
Olga1.956
Sophie1.951
gender
M
by_name
namepopularity
Roland4.263
Gerard3.167
Normand2.901
Angelo2.357
Leo2.307
Marc2.276
Alfred1.968
Peter1.888
Manuel1.818
Raymond1.765
Francis1.642
Maurice1.551
Vincent1.521
Antonio1.476
Anthony1.32
SC
gender
F
by_name
namepopularity
Hattie4.224
Rosa4.094
Maggie3.984
Janie3.949
Annie3.816
Mamie3.753
Daisy3.543
Lillie3.517
Sadie3.125
Carrie3.114
Essie2.804
Lula2.69
Mattie2.63
Willie2.406
Jessie2.266
gender
M
by_name
namepopularity
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Nathaniel4.375
Willie3.948
Roosevelt3.333
Woodrow2.827
Luther2.716
Johnnie2.589
Julius2.571
Leroy2.512
Charlie2.406
Bennie2.345
Freddie2.154
Otis2.133
Horace2.097
Ben1.761
SD
gender
F
by_name
namepopularity
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Karla3.937
Darla3.362
Tamara2.775
Jody2.612
Violet2.566
Mabel2.559
Kay2.521
Delores2.439
Maxine2.348
Verna2.284
Viola2.281
Olive2.173
Leona2.17
Lana2.078
gender
M
by_name
namepopularity
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Darin4.962
Kelly4.236
Duane4.089
Bradley3.211
Brad2.628
Troy2.556
Todd2.431
Delbert2.364
Dean2.301
Dale2.231
Elmer1.909
Daryl1.897
Kim1.862
Virgil1.859
TN
gender
F
by_name
namepopularity
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Bobbie1.82
Wilma1.569
Ruby1.562
Patsy1.538
Billie1.477
Mattie1.452
Glenda1.403
Geneva1.398
Willie1.391
Maggie1.375
Melissa1.251
Wanda1.229
Tonya1.211
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gender
M
by_name
namepopularity
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Jackie2.266
Bobby2.217
Rickey2.011
Donnie1.97
Jimmy1.895
Ronnie1.722
Hubert1.6
Luther1.597
Claude1.538
Tommy1.503
Johnny1.482
Ricky1.479
Hugh1.405
Charlie1.28
TX
gender
F
by_name
namepopularity
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Guadalupe2.744
Yolanda2.695
Billie2.495
Irma2.365
Rosa2.338
Alicia2.303
Janie2.179
Dora1.835
Olga1.779
Bobbie1.6
Patsy1.596
Carmen1.589
Lydia1.579
Juanita1.552
gender
M
by_name
namepopularity
Juan5.606
Jose4.903
Ruben4.508
Manuel4.036
Jesus3.765
Pedro3.677
Roberto3.618
Ricardo3.527
Raul3.493
Carlos3.316
Francisco3.309
Antonio3.274
Hector2.528
Guadalupe2.477
Joe2.128
UT
gender
F
by_name
namepopularity
Robyn5.604
Sherrie4.569
Kristine3.341
Erma3.182
Natalie3.167
Becky3.028
Colleen3.014
Jan2.949
Julie2.747
Jody2.373
Heidi2.303
Lucile2.22
Leslie2.176
Shelly2.124
Teri2.104
gender
M
by_name
namepopularity
Brent10.183
Lynn9
Kelly7.111
Kent7.07
Kim5.931
Darin5.528
Woodrow5.365
Brad4.5
Max4.137
Troy4.03
Rex3.93
Tracy3.246
Kerry3.034
Don2.979
Jay2.955
VA
gender
F
by_name
namepopularity
Nannie1.721
Vanessa1.51
Rosa1.331
Shelia1.329
Lucy1.229
Fannie1.062
Elsie1.055
Susie1.052
Thelma0.97
Hattie0.965
Iris0.964
Nellie0.962
Angela0.928
Virginia0.922
Estelle0.909
gender
M
by_name
namepopularity
Charlie1.762
Clifton1.7
Claude1.626
Lewis1.472
Luther1.403
Calvin1.238
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Otis1.183
Marshall1.115
Cecil1.113
Nelson1.089
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Franklin1.033
Woodrow1.019
Clarence0.974
VT
gender
F
by_name
namepopularity
Madeline4.8
Marion4.135
Laurie4.013
Blanche3.936
Claire3.481
Olive3
Priscilla2.913
Heather2.835
Heidi2.737
Penny2.276
Marguerite2.274
Pauline2.273
Muriel2.269
Tina2.25
Jeannette2.162
gender
M
by_name
namepopularity
Jason4.139
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Clayton2
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WA
gender
F
by_name
namepopularity
Tami2.968
Shannon2.2
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Shelley1.663
Kelli1.655
Colleen1.645
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gender
M
by_name
namepopularity
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WI
gender
F
by_name
namepopularity
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gender
M
by_name
namepopularity
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WV
gender
F
by_name
namepopularity
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Della1.386
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gender
M
by_name
namepopularity
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Clyde1.185
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Danny1.036
Luther1.015
Lewis1.007
WY
gender
F
by_name
namepopularity
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Becky2.798
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Jody2.418
Jamie2.366
Stacy2.313
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gender
M
by_name
namepopularity
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Jason4.431
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Kim3.552
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Kent2.901
Woodrow2.596
Brett2.548
Lance2.536
Bob2.277
Darin2.038
Jim1.959
Bill1.693
Darren1.67
Bradley1.659
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          {
            "name": "Delbert",
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            "name": "Cecil",
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            "name": "Danny",
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    "state": "WY",
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        "gender": "F",
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            "name": "Pat",
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            "name": "Violet",
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            "name": "Jason",
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            "name": "Bob",
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            "name": "Darin",
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            "name": "Jim",
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            "name": "Bill",
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            "name": "Darren",
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          {
            "name": "Bradley",
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]
WITH __stage0 AS (
  SELECT
    group_set,
    (floor(base.year/10))*10 as decade__0,
    base.state as state__0,
    CASE WHEN base.gender='M' THEN 'Male' ELSE 'Female' END as baby_gender__0,
    base.gender as gender__0,
    CASE WHEN group_set=0 THEN
      COALESCE(SUM(base.number),0)
      END as cohort_population__0,
    CASE WHEN group_set=1 THEN
      base.name
      END as name__1,
    CASE WHEN group_set=1 THEN
      COALESCE(SUM(base.number),0)
      END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_current` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,5,7
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    baby_gender__0 as baby_gender,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
      name__1 as name, 
      population__1 as population
      ) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3,4
  ORDER BY 5 desc
)
, __stage2 AS (
  SELECT
    group_set,
    concat(by_name_0.name,'') as name__0,
    names.gender as gender__0,
    floor((CASE WHEN group_set=0 THEN
      COALESCE(SUM(by_name_0.population),0)
      END/CASE WHEN group_set=0 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END)*100000) as births_per_100k__0,
    CASE WHEN group_set=1 THEN
      names.state
      END as state__1,
    floor((CASE WHEN group_set=1 THEN
      COALESCE(SUM(by_name_0.population),0)
      END/CASE WHEN group_set=1 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END)*100000) as births_per_100k__1
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1 as x) as names
  LEFT JOIN UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  WHERE names.decade<1970
  GROUP BY 1,2,3,5
)
, __stage3 AS (
  SELECT
    name__0 as name,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN births_per_100k__0 END) as births_per_100k,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
      state__1 as state, 
      births_per_100k__1 as births_per_100k
      ) END IGNORE NULLS  ORDER BY  births_per_100k__1 desc ) as by_state
  FROM __stage2
  GROUP BY 1,2
  ORDER BY 3 desc
)
, __stage4 AS (
  SELECT
    group_set,
    by_state_0.state as state__0,
    CASE WHEN group_set IN (1,2) THEN
      base.gender
      END as gender__1,
    CASE WHEN group_set=2 THEN
      base.name
      END as name__2,
    CASE WHEN group_set=2 THEN
      ((by_state_0.births_per_100k-base.births_per_100k))/base.births_per_100k
      END as popularity__2
  FROM __stage3 as base
  LEFT JOIN UNNEST(base.by_state) as by_state_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  WHERE base.births_per_100k>50
  GROUP BY 1,2,3,4,5
)
, __stage5 AS (
  SELECT 
    CASE WHEN group_set=2 THEN 1  ELSE group_set END as group_set,
    state__0 as state__0,
    CASE WHEN group_set IN (1,2) THEN
      gender__1
      END as gender__1,
    ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(
      name__2 as name, 
      popularity__2 as popularity
      ) END IGNORE NULLS  ORDER BY  popularity__2 desc  LIMIT 15) as by_name__1
  FROM __stage4
  GROUP BY 1,2,3
)
SELECT
  state__0 as state,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
    gender__1 as gender, 
    by_name__1 as by_name
    ) END IGNORE NULLS  ORDER BY  gender__1 asc ) as by_gender
FROM __stage5
GROUP BY 1
ORDER BY 1 asc